Product data management method and device, storage medium and server

Abstract

The invention discloses a product data management method and device, a storage medium and a server, and relates to the technical field of data management, and the method comprises the steps: obtaining original product data from at least one data source; performing identification, correction and creation processing on the original product data by adopting a fine tuning large language model to obtain product record data, and storing the product record data in a product database; if a natural language instruction of a user is received, performing interactive error correction and operation conversion processing on the natural language instruction by adopting the fine-tuning large language model to obtain a target database management operation; and performing management operation on product record data in the product database based on the target database management operation through a preset system interface. The product data management efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to a product data management method, device, storage medium and server. Background Art

[0002] In modern enterprises, managing and maintaining large amounts of product data is a key challenge, especially in product companies, where product data is diverse and frequently updated. Traditional SQL database management systems (such as MySQL) are powerful but complex and have high barriers to entry. Due to the high complexity of current product data management work, product data management efficiency and accuracy are poor. Summary of the invention

[0003] The embodiment of the present application provides a solution that can improve the efficiency and accuracy of product data management.

[0004] The embodiments of the present application provide the following technical solutions:

[0005] According to one embodiment of the present application, a product data management method includes: obtaining original product data from at least one data source; using a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and storing the product record data in a product database; if a user's natural language instruction is received, using the fine-tuned large language model to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; through a preset system interface, management operations are performed on the product record data in the product database based on the target database management operation.

[0006] In some embodiments of the present application, storing the product record data in a product database includes: obtaining data feature information of the product record data; using a storage resource prediction model to perform prediction processing based on the data feature information to obtain predicted storage resources, wherein the storage resource prediction model is trained using historical usage data of server-side storage resources; adjusting the storage resources of the product database according to the predicted storage resources, and adding the product record data to the product database.

[0007] In some embodiments of the present application, adding the product record data to the product database includes: obtaining a data access log corresponding to the product record data; clustering the product record data according to the data access log to obtain multiple data sets and storage recommendation information for each of the data sets, each of the data sets including at least one product data of the same type; and storing each of the data sets in the product database according to the storage recommendation information of each of the data sets.

[0008] In some embodiments of the present application, the method further includes: interactively learning with the storage environment through reinforcement learning to obtain a storage strategy that adapts to dynamic changes in the storage environment; and adjusting the storage of product data in the product database based on the storage strategy.

[0009] In some embodiments of the present application, after storing the product record data in a product database, the method further includes: using the fine-tuned large language model to monitor the security status of the product data in the product database to obtain data risk conditions and corresponding data security management strategies; issuing early warnings for the data risk conditions, and performing security management of the product data in the product database based on the data security management strategies.

[0010] In some embodiments of the present application, after using the fine-tuned large language model to monitor the security status of the product data in the product database and obtaining the data risk status and the corresponding data security management strategy, the method also includes: obtaining data usage information of the product data in the product database; and performing version management on the product data in the product database based on the data usage information and the data security management strategy.

[0011] In some embodiments of the present application, after storing the product record data in a product database, the method further includes: using the fine-tuned large language model to select a chart type according to data characteristics of the product data to be displayed in the product database to obtain a target chart type; drawing the product data to be displayed as a product display chart of the target chart type; and outputting the product display chart.

[0012] According to one embodiment of the present application, a product data management device includes: an acquisition module, which is used to: acquire original product data from at least one data source; an import module, which is used to: use a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and store the product record data in a product database; a management module, which is used to: if a user's natural language instruction is received, use the fine-tuned large language model to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; an operation module, which is used to: through a preset system interface, perform management operations on the product record data in the product database based on the target database management operation.

[0013] In some embodiments of the present application, the import module is used to: obtain data feature information of the product record data; use a storage resource prediction model to perform prediction processing based on the data feature information to obtain predicted storage resources, and the storage resource prediction model is trained using historical usage data of server-side storage resources; adjust the storage resources of the product database according to the predicted storage resources, and add the product record data to the product database.

[0014] In some embodiments of the present application, the import module is used to: obtain the data access log corresponding to the product record data; cluster the product record data according to the data access log to obtain multiple data sets and storage recommendation information for each data set, each data set including at least one product data of the same type; and store each data set in the product database according to the storage recommendation information of each data set.

[0015] In some embodiments of the present application, the device also includes an optimization module, which is used to: interact with the storage environment through reinforcement learning to obtain a storage strategy that adapts to dynamic changes in the storage environment; and adjust the storage of product data in the product database based on the storage strategy.

[0016] In some embodiments of the present application, after storing the product record data in the product database, the device also includes a security module, which is used to: use the fine-tuned large language model to monitor the security status of the product data in the product database to obtain data risk conditions and corresponding data security management and control strategies; issue early warnings for the data risk conditions, and perform security management and control on the product data in the product database based on the data security management and control strategies.

[0017] In some embodiments of the present application, after the fine-tuned large language model is used to monitor the security status of the product data in the product database to obtain the data risk status and the corresponding data security management strategy, the device also includes a version module, which is used to: obtain data usage information of the product data in the product database; and perform version management on the product data in the product database according to the data usage information and the data security management strategy.

[0018] In some embodiments of the present application, after storing the product record data in the product database, the device also includes a display module, which is used to: use the fine-tuned large language model to select a chart type according to data characteristics of the product data to be displayed in the product database to obtain a target chart type; draw the product data to be displayed as a product display chart of the target chart type; and output the product display chart.

[0019] According to another embodiment of the present application, a storage medium stores a computer program thereon, and when the computer program is executed by a processor of a server, the server executes the method described in the embodiment of the present application.

[0020] According to another embodiment of the present application, a server may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the method described in the embodiment of the present application.

[0021] According to another embodiment of the present application, a computer program product or a computer program includes a computer instruction stored in a computer-readable storage medium. A processor of a server reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the server executes the method provided in various optional implementations described in the embodiments of the present application.

[0022] In an embodiment of the present application, original product data is obtained from at least one data source; a fine-tuned large language model is used to identify, correct and create the original product data to obtain product record data, and the product record data is stored in a product database; if a user's natural language instruction is received, the fine-tuned large language model is used to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; through a preset system interface, management operations are performed on the product record data in the product database based on the target database management operation.

[0023] With the product data management method of the embodiment of the present application, by using a fine-tuned large language model to identify, correct and create original product data, product record data can be obtained efficiently and accurately. After the product record data is stored in the product database, the fine-tuned large language model is further used to perform interactive error correction and operation conversion processing on natural language instructions to obtain accurate and effective target database management operations. Based on the target database management operations, the product record data in the product database can be efficiently and accurately managed, effectively improving the efficiency and accuracy of product data management as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of a product data management method according to an embodiment of the present application is shown.

[0026] Figure 2 A storage management flow chart according to an embodiment of the present application is shown.

[0027] Figure 3 A storage management flowchart according to another embodiment of the present application is shown.

[0028] Figure 4 A block diagram of a product data management device according to an embodiment of the present application is shown.

[0029] Figure 5 A block diagram of a server according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] The present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are only used to explain the present disclosure and are not intended to limit the present disclosure. In addition, the embodiments provided below are partial embodiments for implementing the present disclosure, rather than providing all embodiments for implementing the present disclosure. In the absence of conflict, the technical solutions recorded in the embodiments of the present disclosure can be implemented in any combination.

[0031] It should be noted that, in the embodiments of the present disclosure, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method or apparatus including a series of elements includes not only the elements explicitly recorded, but also includes other elements not explicitly listed, or also includes elements inherent to the implementation of the method or apparatus. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other related elements in the method or apparatus including the element (such as a step in the method or a unit in the apparatus, for example, a unit may be a part of a circuit, a part of a processor, a part of a program or software, etc.).

[0032] For example, the product data management method provided by the embodiment of the present disclosure includes a series of steps, but the product data management method provided by the embodiment of the present disclosure is not limited to the recorded steps. Similarly, the product data management device provided by the embodiment of the present disclosure includes a series of units, but the device provided by the embodiment of the present disclosure is not limited to including the units explicitly recorded, and may also include units required to obtain relevant information or perform processing based on the information.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0034] It is understandable that in the specific implementation of this application, relevant data is involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0035] Figure 1 The flowchart of a product data management method according to an embodiment of the present application is schematically shown. The execution subject of the product data management method may be a server with processing capabilities, such as a cloud server, a physical server or a server cluster.

[0036] like Figure 1 As shown, the product data management method may include steps S110 to S140.

[0037] Step S110, obtaining original product data from at least one data source; Step S120, using a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and storing the product record data in a product database; Step S130, if a user's natural language instruction is received, using the fine-tuned large language model to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; Step S140, through a preset system interface, management operations are performed on the product record data in the product database based on the target database management operation.

[0038] Different data sources may refer to the sources of original product data such as different platforms or systems. The original product data may include various attributes of the product (such as name, price, etc.). The format, quantity, and type of the original product data in different data sources may be the same or different.

[0039] After obtaining the original product data from at least one data source, the fine-tuned large language model is used to identify, correct and create the obtained original product data to obtain created product record data. The product record data includes one or more product data, each of which can be the record data of a product, and each product data can include attribute data such as product name, description, price, inventory, color, size, weight, etc.

[0040] The fine-tuned large language model is obtained by fine-tuning the existing large language model (LLM). The fine-tuned large language model can identify, correct and create original product data from different sources, different formats, various defects, etc., and obtain product record data efficiently and accurately. Among them, the obtained original product data can be guaranteed in the form of a table or other data structure, and input into the fine-tuned large language model in the form of a table or other data structure.

[0041] Specifically, by using a fine-tuned large language model, the original product data can be first identified. Through identification, the table structure, data type and content of the original product data can be parsed to obtain the data therein and convert it into initial product data in a predetermined format (one or more initial product data, each of which may include text, numbers, dates, etc.); then, the initial product data can be corrected. Through error correction, the initial product data can be cleaned and verified to automatically detect and correct errors in the data (such as missing data, spelling errors, inconsistent data formats, etc.), thereby obtaining corrected product data; then, product record data is created based on the corrected product data.

[0042] The created product record data is added to the preset product database. After receiving the user's natural language instructions, the fine-tuned large language model can be further used to perform interactive error correction and operation conversion processing on the natural language instructions, that is, the natural language instructions are corrected by interacting with the user and the corrected instructions are converted into database management operations. This allows accurate and effective target database management operations (for example, create, read, update and / or delete operations) to be obtained conveniently and efficiently according to user needs, and non-technical users can also quickly perform accurate and effective management according to their needs.

[0043] Specifically, the user inputs natural language instructions to the server through the user terminal, and the server uses the fine-tuned large language model to interactively correct the natural language instructions (for example, the server communicates with the user through a dialogue interface to provide intelligent instruction correction and suggestions), and then converts the final corrected instructions into a target database management operation in a standard format through operation conversion processing.

[0044] Furthermore, the product data management method of the embodiment of the present application can be specifically applied in a product data management system. A preset system interface is provided in the product data management system (for example, an enterprise knowledge management and collaboration system (Confluence), a customer relationship management system (CRM), an enterprise resource planning (ERP) system, and a content management system (CMS), etc.). Through the preset system interface, management operations can be performed on product record data in a product database based on the target database management operation (the target database management operation can be a CRUD operation encapsulated in Python or a database operation encapsulated in other languages), thereby further realizing efficient and accurate product data management according to user needs.

[0045] In summary, with the product data management method of the embodiment of the present application, by using a fine-tuned large language model to identify, correct and create original product data, product record data can be obtained efficiently and accurately. After the product record data is stored in the product database, the fine-tuned large language model is further used to perform interactive error correction and operation conversion processing on natural language instructions to obtain accurate and effective target database management operations. Based on the target database management operations, the product record data in the product database can be efficiently and accurately managed, effectively improving the efficiency and accuracy of product data management as a whole.

[0046] Described below Figure 1 When product data management is performed under the embodiment, further optional specific embodiments are provided under each step performed.

[0047] In one embodiment, see Figure 2 , storing the product record data into a product database may include: step S210, obtaining data feature information of the product record data; step S220, using a storage resource prediction model to perform prediction processing based on the data feature information to obtain predicted storage resources, wherein the storage resource prediction model is trained using historical usage data of server-side storage resources; step S230, adjusting the storage resources of the product database according to the predicted storage resources, and adding the product record data to the product database.

[0048] For product record data, its data feature information can be obtained, and the data feature information can be one or more predetermined attribute data of each product data. A storage resource prediction model is used to perform prediction processing based on the data feature information to obtain predicted storage resources, and the storage resources of the product database are adjusted according to the predicted storage resources, and the product record data is added to the product database. Therefore, the entire system can automatically adjust the storage resources according to the predicted storage resources to ensure the reasonable allocation and efficient use of storage space.

[0049] The storage resource prediction model is trained using the historical usage data of the server-side storage resources (i.e., the storage resources in the server), that is, the storage resource prediction model uses the historical usage data to predict future storage needs to obtain the predicted storage resources. The storage resource prediction model can be a time series model.

[0050] In one embodiment, see Figure 3, the adding of the product record data to the product database includes: step S310, obtaining a data access log corresponding to the product record data; step S320, clustering the product record data according to the data access log to obtain multiple data sets and storage recommendation information for each data set, each data set including at least one product data of the same type; step S330, storing each data set in the product database according to the storage recommendation information of each data set.

[0051] The data access log corresponding to the product record data is obtained, and the product record data is clustered according to the data access log. The product data in the product record data can be clustered into different important types according to the access frequency, and multiple data sets of different important types and storage recommendation information of each data set are obtained, and each data set includes at least one product data of the same type (i.e., the same important type). The storage recommendation information for each data set is the recommended storage strategy for the product data in each data set.

[0052] For example, there are two data sets, namely, a hot data set and a cold data set. The storage recommendation information of the hot data set is to store it on a high-performance storage medium, and the storage recommendation information of the cold data set is to store it on a low-cost storage medium. Therefore, by clustering data and making storage recommendations by category, the configuration and use efficiency of storage resources can be further optimized.

[0053] Furthermore, in one embodiment, the method may also include: interactively learning with the environment through reinforcement learning to obtain a storage strategy that adapts to dynamic changes in the environment; and adjusting the storage of product data in the product database based on the storage strategy.

[0054] The product data management method of the embodiment of the present application can be specifically executed in a server, such as a cloud server, and the product data management method of the embodiment of the present application can be specifically applied to a product data management system deployed in the server. Servers of different platforms are different environments, for example, cloud servers of AWS, Google Cloud, and Azure can each be an environment.

[0055] The product data management system can continuously interact and learn with the environment in a dynamic environment through reinforcement learning (Q-learning). By designing a reward function that measures the effectiveness of storage strategies and maximizing the accumulated rewards of different storage strategies, the optimal storage strategy that adapts to the dynamic changes of the environment is obtained. This storage strategy that adapts to the dynamic changes of the environment can then be applied to the product data management system to perform storage management of product data. Due to the real-time dynamic reinforcement learning of the optimal strategy and adaptation to the dynamic changes of the environment, the reliability and efficiency of product data management can be further improved.

[0056] Furthermore, in one embodiment, after storing the product record data in the product database, the method also includes: using the fine-tuned large language model to monitor the security status of the product data in the product database to obtain data risk conditions and corresponding data security management strategies; issuing early warnings for the data risk conditions, and performing security management of the product data in the product database based on the data security management strategies.

[0057] For product data stored in the product database, during use, a fine-tuned large language model can be used to monitor the access data of product data in the product database in real time and dynamically, and the data risk situation of the product data and the corresponding data security management and control strategies (such as strengthening access control and encrypted storage management and control strategies) can be obtained. Then, early warnings can be issued for data risk situations, and security management and control of product data in the product database can be performed based on the data security management and control strategies (such as limiting the number of accesses, data encryption, etc.), further improving product data security.

[0058] Furthermore, in one embodiment, after using the fine-tuned large language model to monitor the security status of product data in the product database and obtaining data risk status and corresponding data security management and control strategies, the method may also include: obtaining data usage information of the product data in the product database; and performing version management on the product data in the product database based on the data usage information and the data security management and control strategies.

[0059] From the preset version control methods (which may include version management methods matched by different combinations of data usage information and data security control policies), the version management method that matches the current data usage information and data security control policies can be determined, and the product data in the product database can be version managed according to the matched version management method. For example, product data backup or recovery management can be performed to form different versions of data. Version management of product data based on data usage information and data security control policies can further improve the reliability of product data management.

[0060] Furthermore, in one embodiment, after storing the product record data in the product database, the method may also include: using the fine-tuned large language model to select a chart type according to data characteristics of the product data to be displayed in the product database to obtain a target chart type; drawing the product data to be displayed as a product display chart of the target chart type; and outputting the product display chart.

[0061] The product data to be displayed can be product data requested by users, updated product data, or all product data in the product database, etc. For the product data to be displayed, a fine-tuned large language model is used to select a chart type according to the data characteristics of the product data to be displayed, and an icon type adapted to the data characteristics of the product data to be displayed is obtained as the target chart type.

[0062] Then, the product data to be displayed is drawn into a product display chart of the target chart type (such as a product sales trend chart, etc.), and the product display chart is output to the user terminal for display, so that the data can be better displayed according to the characteristics of the product data itself, further improving the product data management effect.

[0063] Furthermore, in some embodiments, after storing the product record data in the product database, the method may further include: receiving a product data display language instruction sent by a user, using a fine-tuned large language model to draw a product data graph according to the product data display language instruction, and outputting the drawn product data graph. Thus, the user can customize the chart display through natural language instructions.

[0064] In order to better implement the product data management method provided in the embodiment of the present application, the embodiment of the present application also provides a product data management device based on the above product data management method. The meanings of the terms are the same as those in the above product data management method, and the specific implementation details can refer to the description in the method embodiment. Figure 4 A block diagram of a product data management device according to an embodiment of the present application is shown.

[0065] like Figure 4 As shown, the product data management device 400 may include: an acquisition module 410 can be used to: acquire original product data from at least one data source; an import module 420 can be used to: use a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and store the product record data in a product database; a management module 430 can be used to: if a user's natural language instruction is received, use the fine-tuned large language model to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; an operation module 440 can be used to: through a preset system interface, perform management operations on the product record data in the product database based on the target database management operation.

[0066] In some embodiments of the present application, the import module is used to: obtain data feature information of the product record data; use a storage resource prediction model to perform prediction processing based on the data feature information to obtain predicted storage resources, and the storage resource prediction model is trained using historical usage data of server-side storage resources; adjust the storage resources of the product database according to the predicted storage resources, and add the product record data to the product database.

[0067] In some embodiments of the present application, the import module is used to: obtain the data access log corresponding to the product record data; cluster the product record data according to the data access log to obtain multiple data sets and storage recommendation information for each data set, each data set including at least one product data of the same type; and store each data set in the product database according to the storage recommendation information of each data set.

[0068] In some embodiments of the present application, the device also includes an optimization module, which is used to: interact with the storage environment through reinforcement learning to obtain a storage strategy that adapts to dynamic changes in the storage environment; and adjust the storage of product data in the product database based on the storage strategy.

[0069] In some embodiments of the present application, after storing the product record data in the product database, the device also includes a security module, which is used to: use the fine-tuned large language model to monitor the security status of the product data in the product database to obtain data risk conditions and corresponding data security management and control strategies; issue early warnings for the data risk conditions, and perform security management and control on the product data in the product database based on the data security management and control strategies.

[0070] In some embodiments of the present application, after the fine-tuned large language model is used to monitor the security status of the product data in the product database to obtain the data risk status and the corresponding data security management strategy, the device also includes a version module, which is used to: obtain data usage information of the product data in the product database; and perform version management on the product data in the product database according to the data usage information and the data security management strategy.

[0071] In some embodiments of the present application, after storing the product record data in the product database, the device also includes a display module, which is used to: use the fine-tuned large language model to select a chart type according to data characteristics of the product data to be displayed in the product database to obtain a target chart type; draw the product data to be displayed as a product display chart of the target chart type; and output the product display chart.

[0072] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0073] In addition, the present application also provides a server, such as Figure 5 As shown, Figure 5 A block diagram of a server according to an embodiment of the present application is shown, specifically:

[0074] The server may include one or more processors 501 of processing cores, one or more computer-readable storage media memories 502 and other components. Those skilled in the art will appreciate that Figure 5 The server structure shown in the figure does not constitute a limitation on the server, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0075] Specifically in this embodiment, the processor 501 in the server can load the executable files corresponding to the processes of one or more programs into the memory 502 according to the instructions, and the processor 501 can run the programs stored in the memory 502, so as to realize the various functions in the aforementioned embodiments of the present application.

[0076] For example, the processor 501 may execute the following steps:

[0077] Original product data is obtained from at least one data source; the original product data is identified, corrected and created using a fine-tuned large language model to obtain product record data, and the product record data is stored in a product database; if a natural language instruction from a user is received, the natural language instruction is interactively corrected and converted using the fine-tuned large language model to obtain a target database management operation; and management operations are performed on the product record data in the product database based on the target database management operation through a preset system interface.

[0078] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by a computer program, or by controlling related hardware through a computer program. The computer program may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0079] To this end, an embodiment of the present application further provides a storage medium, in which a computer program is stored. The computer program can be loaded by a processor to execute the steps in any method provided in the embodiment of the present application.

[0080] The storage medium may be a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0081] Since the computer program stored in the storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by the method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0082] Those skilled in the art will readily come up with other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0083] It should be understood that the present application is not limited to the embodiments that have been described above and shown in the accompanying drawings, but various modifications and changes may be made without departing from the scope thereof.

Claims

1. A product data management method, characterized in that: include: Obtaining raw product data from at least one data source; Using a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and storing the product record data in a product database; If a natural language instruction from a user is received, the fine-tuned large language model is used to perform interactive error correction and operation conversion processing on the natural language instruction to obtain a target database management operation; Through a preset system interface, management operations are performed on the product record data in the product database based on the target database management operations.

2. The method according to claim 1, characterized in that The storing of the product record data into a product database comprises: Acquire data characteristic information of the product record data; Using a storage resource prediction model to perform prediction processing based on the data feature information to obtain predicted storage resources, wherein the storage resource prediction model is trained using historical usage data of server-side storage resources; The storage resources of the product database are adjusted according to the predicted storage resources, and the product record data is added to the product database.

3. The method according to claim 2, characterized in that The adding the product record data to the product database comprises: Obtaining a data access log corresponding to the product record data; Clustering the product record data according to the data access log to obtain multiple data sets and storage recommendation information for each of the data sets, each of the data sets including at least one product data of the same type; According to the storage suggestion information of each data set, each data set is stored in the product database respectively.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Through reinforcement learning and interactive learning with the storage environment, a storage strategy that adapts to the dynamic changes of the storage environment is obtained; The product data in the product database is adjusted for storage based on the storage policy.

5. The method according to claim 1, characterized in that After storing the product record data in a product database, the method further includes: Using the fine-tuned large language model to monitor the security status of product data in the product database, and obtaining data risk status and corresponding data security management and control strategies; An early warning is issued for the data risk situation, and security management and control is performed on the product data in the product database based on the data security management and control strategy.

6. The method according to claim 5, characterized in that After the fine-tuned large language model is used to monitor the security status of the product data in the product database to obtain the data risk status and the corresponding data security management and control strategy, the method further includes: Acquire data usage information of product data in the product database; Version management is performed on the product data in the product database according to the data usage information and the data security management and control strategy.

7. The method according to claim 1, characterized in that After storing the product record data in a product database, the method further includes: Using the fine-tuned large language model to select a chart type according to data characteristics of the product data to be displayed in the product database, to obtain a target chart type; Draw the product data to be displayed into a product display chart of the target chart type; Output the product display chart.

8. A product data management device, characterized in that: include: An acquisition module, used to: acquire original product data from at least one data source; An import module is used to: use a fine-tuned large language model to identify, correct and create the original product data to obtain product record data, and store the product record data in a product database; A management module is used to: upon receiving a natural language instruction from a user, perform interactive error correction and operation conversion processing on the natural language instruction using the fine-tuned large language model to obtain a target database management operation; The operation module is used to: perform management operations on the product record data in the product database based on the target database management operation through a preset system interface.

9. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A server, characterized in that: include: a memory storing a computer program; A processor reads a computer program stored in a memory to execute the method according to any one of claims 1 to 7.

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